Predictive Analytics and the Targeting of Audits

Hashimzade, N.; Myles, G.D.; Rablen, M.D.

Abstract

The literature on audit strategies has focused on random audits or on audits conditioned only on income declaration. In contrast, tax authorities employ the tools of predictive analytics to identify taxpayers for audit, with a range of variables used for conditioning. The paper explores the compliance and revenue consequences of the use of predictive analytics in an agent-based model that draws upon a behavioral approach to tax compliance. The taxpayers in the model form subjective beliefs about the probability of audit from social interaction, and are guided by a social custom that is developed from meeting other taxpayers. The belief and social custom feed into the occupational choice between employment and two forms of self-employment. It is shown that the use of predictive analytics yields a significant increase in revenue over a random audit strategy.

Citation

Hashimzade, N., Myles, G., & Rablen, M. (2016). Predictive Analytics and the Targeting of Audits. Journal of Economic Behavior and Organization, 124, 130-145. https://doi.org/10.1016/j.jebo.2015.11.009

• Journal Article Type: Article
• Acceptance Date: Nov 19, 2015
• Online Publication Date: Dec 8, 2015
• Publication Date: Apr 1, 2016
• Deposit Date: Nov 20, 2015
• Publicly Available Date: Jun 8, 2017
• Journal: Journal of Economic Behavior and Organization
• Print ISSN: 0167-2681
• Electronic ISSN: 2328-7616
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 124
• Pages: 130-145
• DOI: https://doi.org/10.1016/j.jebo.2015.11.009
• Keywords: Tax compliance, Social network, Agent-based model.
• Public URL: https://durham-repository.worktribe.com/output/1418048